stMale)Geiser C. Challco geiser@alumni.usp.br
env <- "stMale"
gender <- "women"
to_remove <- c('S11')
sub.groups <- c("country","age","ed.level","intervention",
"country:age","country:ed.level","country:intervention",
"age:intervention","ed.level:intervention",
"country:age:intervention","country:ed.level:intervention")dat <- read_excel("../data/data-without-outliers.xlsx", sheet = "perform-env.gender-descriptive")
dat <- dat[!dat$study %in% to_remove, ]
leg <- read_excel("../data/data-without-outliers.xlsx", sheet = "legend")## New names:
## • `` -> `...10`leg <- leg[!leg$study %in% to_remove, ]
idx.e <- which(dat$env == env & dat$gender == gender)
idx.c <- which(dat$env == "control" & dat$gender == gender)
data <- data.frame(
study = dat$study[idx.c],
n.e = dat$N[idx.e], mean.e = dat$M[idx.e], sd.e = dat$SD[idx.e],
n.c = dat$N[idx.c], mean.c = dat$M[idx.c], sd.c = dat$SD[idx.c]
)
for (cgroups in strsplit(sub.groups,":")) {
data[[paste0(cgroups, collapse = ":")]] <- sapply(data$study, FUN = function(x) {
paste0(sapply(cgroups, FUN = function(namecol) leg[[namecol]][which(x == leg$study)]), collapse = ":")
})
}
data[["lbl"]] <- sapply(data$study, FUN = function(x) leg$Note[which(x == leg$study)])m.cont <- metacont(
n.e = n.e, mean.e = mean.e, sd.e = sd.e, n.c = n.c, mean.c = mean.c, sd.c = sd.c,
studlab = lbl, data = data, sm = "SMD", method.smd = "Hedges",
fixed = F, random = T, method.tau = "REML", hakn = T, title = paste("Performance for",gender,"in",env)
)
summary(m.cont)## Review: Performance for women in stMale
##
## SMD 95%-CI %W(random)
## S1 -0.3420 [-0.9865; 0.3026] 10.5
## S2 -0.0614 [-0.6009; 0.4780] 15.0
## S3 -0.3018 [-0.9889; 0.3852] 9.2
## S4 0.4275 [-0.2948; 1.1499] 8.4
## S5 0.1428 [-0.5485; 0.8341] 9.1
## S6 0.4443 [-0.2942; 1.1829] 8.0
## S7 0.4315 [-0.1676; 1.0305] 12.2
## S8: Conducted by BNU 0.0743 [-0.5888; 0.7374] 9.9
## S9: Albuquerque, et al. (2017) -0.3727 [-1.1083; 0.3628] 8.1
## S10: Only use prompt msgs 0.2941 [-0.3794; 0.9675] 9.6
##
## Number of studies combined: k = 10
## Number of observations: o = 360
##
## SMD 95%-CI t p-value
## Random effects model 0.0694 [-0.1595; 0.2982] 0.69 0.5101
##
## Quantifying heterogeneity:
## tau^2 = 0 [0.0000; 0.2388]; tau = 0 [0.0000; 0.4887]
## I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
##
## Test of heterogeneity:
## Q d.f. p-value
## 8.11 9 0.5232
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.cont, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.sg4sub <- update.meta(m.cont, subgroup = country, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance for women in stMale
##
## SMD 95%-CI %W(random) country
## S1 -0.3420 [-0.9865; 0.3026] 10.5 Brazil
## S2 -0.0614 [-0.6009; 0.4780] 15.0 Brazil
## S3 -0.3018 [-0.9889; 0.3852] 9.2 Brazil
## S4 0.4275 [-0.2948; 1.1499] 8.4 Brazil
## S5 0.1428 [-0.5485; 0.8341] 9.1 Brazil
## S6 0.4443 [-0.2942; 1.1829] 8.0 Brazil
## S7 0.4315 [-0.1676; 1.0305] 12.2 Brazil
## S8: Conducted by BNU 0.0743 [-0.5888; 0.7374] 9.9 China
## S9: Albuquerque, et al. (2017) -0.3727 [-1.1083; 0.3628] 8.1 Brazil
## S10: Only use prompt msgs 0.2941 [-0.3794; 0.9675] 9.6 Brazil
##
## Number of studies combined: k = 10
## Number of observations: o = 360
##
## SMD 95%-CI t p-value
## Random effects model 0.0694 [-0.1595; 0.2982] 0.69 0.5101
##
## Quantifying heterogeneity:
## tau^2 = 0 [0.0000; 0.2388]; tau = 0 [0.0000; 0.4887]
## I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
##
## Test of heterogeneity:
## Q d.f. p-value
## 8.11 9 0.5232
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q I^2
## country = Brazil 9 0.0688 [-0.1919; 0.3295] <0.0001 0.0014 8.11 1.3%
## country = China 1 0.0743 [-0.5888; 0.7374] -- -- 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 0.00 1 0.9877
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.sg4sub <- update.meta(m.cont, subgroup = country, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance for women in stMale
##
## SMD 95%-CI %W(random) country
## S1 -0.3420 [-0.9865; 0.3026] 10.5 Brazil
## S2 -0.0614 [-0.6009; 0.4780] 15.0 Brazil
## S3 -0.3018 [-0.9889; 0.3852] 9.2 Brazil
## S4 0.4275 [-0.2948; 1.1499] 8.4 Brazil
## S5 0.1428 [-0.5485; 0.8341] 9.1 Brazil
## S6 0.4443 [-0.2942; 1.1829] 8.0 Brazil
## S7 0.4315 [-0.1676; 1.0305] 12.2 Brazil
## S8: Conducted by BNU 0.0743 [-0.5888; 0.7374] 9.9 China
## S9: Albuquerque, et al. (2017) -0.3727 [-1.1083; 0.3628] 8.1 Brazil
## S10: Only use prompt msgs 0.2941 [-0.3794; 0.9675] 9.6 Brazil
##
## Number of studies combined: k = 10
## Number of observations: o = 360
##
## SMD 95%-CI t p-value
## Random effects model 0.0694 [-0.1595; 0.2982] 0.69 0.5101
##
## Quantifying heterogeneity:
## tau^2 = 0 [0.0000; 0.2388]; tau = 0 [0.0000; 0.4887]
## I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
##
## Test of heterogeneity:
## Q d.f. p-value
## 8.11 9 0.5232
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q I^2
## country = Brazil 9 0.0688 [-0.1919; 0.3295] <0.0001 0.0014 8.11 1.3%
## country = China 1 0.0743 [-0.5888; 0.7374] -- -- 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 0.00 1 0.9877
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.sg4sub <- update.meta(m.cont, subgroup = ed.level, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance for women in stMale
##
## SMD 95%-CI %W(random) ed.level
## S1 -0.3420 [-0.9865; 0.3026] 10.5 upper-secundary
## S2 -0.0614 [-0.6009; 0.4780] 15.0 upper-secundary
## S3 -0.3018 [-0.9889; 0.3852] 9.2 upper-secundary
## S4 0.4275 [-0.2948; 1.1499] 8.4 higher-education
## S5 0.1428 [-0.5485; 0.8341] 9.1 higher-education
## S6 0.4443 [-0.2942; 1.1829] 8.0 higher-education
## S7 0.4315 [-0.1676; 1.0305] 12.2 unknown
## S8: Conducted by BNU 0.0743 [-0.5888; 0.7374] 9.9 unknown
## S9: Albuquerque, et al. (2017) -0.3727 [-1.1083; 0.3628] 8.1 unknown
## S10: Only use prompt msgs 0.2941 [-0.3794; 0.9675] 9.6 upper-secundary
##
## Number of studies combined: k = 10
## Number of observations: o = 360
##
## SMD 95%-CI t p-value
## Random effects model 0.0694 [-0.1595; 0.2982] 0.69 0.5101
##
## Quantifying heterogeneity:
## tau^2 = 0 [0.0000; 0.2388]; tau = 0 [0.0000; 0.4887]
## I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
##
## Test of heterogeneity:
## Q d.f. p-value
## 8.11 9 0.5232
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q I^2
## ed.level = upper-secundary 4 -0.1008 [-0.5377; 0.3360] 0 0 2.21 0.0%
## ed.level = higher-education 3 0.3308 [-0.0970; 0.7586] 0 0 0.44 0.0%
## ed.level = unknown 3 0.0840 [-0.9049; 1.0730] 0.0431 0.2077 2.77 27.8%
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 6.65 2 0.0360
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.sg4sub <- update.meta(m.cont, subgroup = intervention, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance for women in stMale
##
## SMD 95%-CI %W(random)
## S1 -0.3420 [-0.9865; 0.3026] 10.5
## S2 -0.0614 [-0.6009; 0.4780] 15.0
## S3 -0.3018 [-0.9889; 0.3852] 9.2
## S4 0.4275 [-0.2948; 1.1499] 8.4
## S5 0.1428 [-0.5485; 0.8341] 9.1
## S6 0.4443 [-0.2942; 1.1829] 8.0
## S7 0.4315 [-0.1676; 1.0305] 12.2
## S8: Conducted by BNU 0.0743 [-0.5888; 0.7374] 9.9
## S9: Albuquerque, et al. (2017) -0.3727 [-1.1083; 0.3628] 8.1
## S10: Only use prompt msgs 0.2941 [-0.3794; 0.9675] 9.6
## intervention
## S1 Gender-stereotype color, ranking, badges, and avatar
## S2 Gender-stereotype color, ranking, badges, and avatar
## S3 Gender-stereotype color, ranking, badges, and avatar
## S4 Gender-stereotype color, ranking, badges, and avatar
## S5 Gender-stereotype color, ranking, badges, and avatar
## S6 Gender-stereotype color, ranking, badges, and avatar
## S7 Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017) Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs Gender-stereotyped motivational message prompts
##
## Number of studies combined: k = 10
## Number of observations: o = 360
##
## SMD 95%-CI t p-value
## Random effects model 0.0694 [-0.1595; 0.2982] 0.69 0.5101
##
## Quantifying heterogeneity:
## tau^2 = 0 [0.0000; 0.2388]; tau = 0 [0.0000; 0.4887]
## I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
##
## Test of heterogeneity:
## Q d.f. p-value
## 8.11 9 0.5232
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q I^2
## intervention = Gender-stereotype color, rankin ... 9 0.0455 [-0.2071; 0.2980] 0 0 7.64 0.0%
## intervention = Gender-stereotyped motivational ... 1 0.2941 [-0.3794; 0.9675] -- -- 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 0.48 1 0.4906
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.sg4sub <- update.meta(m.cont, subgroup = `country:age`, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance for women in stMale
##
## SMD 95%-CI %W(random) country:age
## S1 -0.3420 [-0.9865; 0.3026] 10.5 Brazil:adolescent
## S2 -0.0614 [-0.6009; 0.4780] 15.0 Brazil:adolescent
## S3 -0.3018 [-0.9889; 0.3852] 9.2 Brazil:adolescent
## S4 0.4275 [-0.2948; 1.1499] 8.4 Brazil:adult
## S5 0.1428 [-0.5485; 0.8341] 9.1 Brazil:adult
## S6 0.4443 [-0.2942; 1.1829] 8.0 Brazil:adult
## S7 0.4315 [-0.1676; 1.0305] 12.2 Brazil:adult
## S8: Conducted by BNU 0.0743 [-0.5888; 0.7374] 9.9 China:no-restriction
## S9: Albuquerque, et al. (2017) -0.3727 [-1.1083; 0.3628] 8.1 Brazil:no-restriction
## S10: Only use prompt msgs 0.2941 [-0.3794; 0.9675] 9.6 Brazil:adolescence
##
## Number of studies combined: k = 10
## Number of observations: o = 360
##
## SMD 95%-CI t p-value
## Random effects model 0.0694 [-0.1595; 0.2982] 0.69 0.5101
##
## Quantifying heterogeneity:
## tau^2 = 0 [0.0000; 0.2388]; tau = 0 [0.0000; 0.4887]
## I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
##
## Test of heterogeneity:
## Q d.f. p-value
## 8.11 9 0.5232
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q I^2
## country:age = Brazil:adolescent 3 -0.2102 [-0.6073; 0.1869] 0 0 0.52 0.0%
## country:age = Brazil:adult 4 0.3633 [ 0.1338; 0.5928] 0 0 0.52 0.0%
## country:age = China:no-restriction 1 0.0743 [-0.5888; 0.7374] -- -- 0.00 --
## country:age = Brazil:no-restriction 1 -0.3727 [-1.1083; 0.3628] -- -- 0.00 --
## country:age = Brazil:adolescence 1 0.2941 [-0.3794; 0.9675] -- -- 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 26.09 4 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.sg4sub <- update.meta(m.cont, subgroup = `country:ed.level`, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance for women in stMale
##
## SMD 95%-CI %W(random) country:ed.level
## S1 -0.3420 [-0.9865; 0.3026] 10.5 Brazil:upper-secundary
## S2 -0.0614 [-0.6009; 0.4780] 15.0 Brazil:upper-secundary
## S3 -0.3018 [-0.9889; 0.3852] 9.2 Brazil:upper-secundary
## S4 0.4275 [-0.2948; 1.1499] 8.4 Brazil:higher-education
## S5 0.1428 [-0.5485; 0.8341] 9.1 Brazil:higher-education
## S6 0.4443 [-0.2942; 1.1829] 8.0 Brazil:higher-education
## S7 0.4315 [-0.1676; 1.0305] 12.2 Brazil:unknown
## S8: Conducted by BNU 0.0743 [-0.5888; 0.7374] 9.9 China:unknown
## S9: Albuquerque, et al. (2017) -0.3727 [-1.1083; 0.3628] 8.1 Brazil:unknown
## S10: Only use prompt msgs 0.2941 [-0.3794; 0.9675] 9.6 Brazil:upper-secundary
##
## Number of studies combined: k = 10
## Number of observations: o = 360
##
## SMD 95%-CI t p-value
## Random effects model 0.0694 [-0.1595; 0.2982] 0.69 0.5101
##
## Quantifying heterogeneity:
## tau^2 = 0 [0.0000; 0.2388]; tau = 0 [0.0000; 0.4887]
## I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
##
## Test of heterogeneity:
## Q d.f. p-value
## 8.11 9 0.5232
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q I^2
## country:ed.level = Brazil:upper-secundary 4 -0.1008 [-0.5377; 0.3360] 0 0 2.21 0.0%
## country:ed.level = Brazil:higher-education 3 0.3308 [-0.0970; 0.7586] 0 0 0.44 0.0%
## country:ed.level = Brazil:unknown 2 0.0589 [-5.0366; 5.1543] 0.2062 0.4541 2.76 63.8%
## country:ed.level = China:unknown 1 0.0743 [-0.5888; 0.7374] -- -- 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 6.66 3 0.0834
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.sg4sub <- update.meta(m.cont, subgroup = `country:intervention`, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance for women in stMale
##
## SMD 95%-CI %W(random)
## S1 -0.3420 [-0.9865; 0.3026] 10.5
## S2 -0.0614 [-0.6009; 0.4780] 15.0
## S3 -0.3018 [-0.9889; 0.3852] 9.2
## S4 0.4275 [-0.2948; 1.1499] 8.4
## S5 0.1428 [-0.5485; 0.8341] 9.1
## S6 0.4443 [-0.2942; 1.1829] 8.0
## S7 0.4315 [-0.1676; 1.0305] 12.2
## S8: Conducted by BNU 0.0743 [-0.5888; 0.7374] 9.9
## S9: Albuquerque, et al. (2017) -0.3727 [-1.1083; 0.3628] 8.1
## S10: Only use prompt msgs 0.2941 [-0.3794; 0.9675] 9.6
## country:intervention
## S1 Brazil:Gender-stereotype color, ranking, badges, and avatar
## S2 Brazil:Gender-stereotype color, ranking, badges, and avatar
## S3 Brazil:Gender-stereotype color, ranking, badges, and avatar
## S4 Brazil:Gender-stereotype color, ranking, badges, and avatar
## S5 Brazil:Gender-stereotype color, ranking, badges, and avatar
## S6 Brazil:Gender-stereotype color, ranking, badges, and avatar
## S7 Brazil:Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU China:Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017) Brazil:Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs Brazil:Gender-stereotyped motivational message prompts
##
## Number of studies combined: k = 10
## Number of observations: o = 360
##
## SMD 95%-CI t p-value
## Random effects model 0.0694 [-0.1595; 0.2982] 0.69 0.5101
##
## Quantifying heterogeneity:
## tau^2 = 0 [0.0000; 0.2388]; tau = 0 [0.0000; 0.4887]
## I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
##
## Test of heterogeneity:
## Q d.f. p-value
## 8.11 9 0.5232
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q
## country:intervention = Brazil:Gender-stereotype color, ... 8 0.0421 [-0.2519; 0.3361] 0.0069 0.0829 7.63
## country:intervention = China:Gender-stereotype color, ... 1 0.0743 [-0.5888; 0.7374] -- -- 0.00
## country:intervention = Brazil:Gender-stereotyped motiv ... 1 0.2941 [-0.3794; 0.9675] -- -- 0.00
## I^2
## country:intervention = Brazil:Gender-stereotype color, ... 8.2%
## country:intervention = China:Gender-stereotype color, ... --
## country:intervention = Brazil:Gender-stereotyped motiv ... --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 0.48 2 0.7884
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.sg4sub <- update.meta(m.cont, subgroup = `age:intervention`, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance for women in stMale
##
## SMD 95%-CI %W(random)
## S1 -0.3420 [-0.9865; 0.3026] 10.5
## S2 -0.0614 [-0.6009; 0.4780] 15.0
## S3 -0.3018 [-0.9889; 0.3852] 9.2
## S4 0.4275 [-0.2948; 1.1499] 8.4
## S5 0.1428 [-0.5485; 0.8341] 9.1
## S6 0.4443 [-0.2942; 1.1829] 8.0
## S7 0.4315 [-0.1676; 1.0305] 12.2
## S8: Conducted by BNU 0.0743 [-0.5888; 0.7374] 9.9
## S9: Albuquerque, et al. (2017) -0.3727 [-1.1083; 0.3628] 8.1
## S10: Only use prompt msgs 0.2941 [-0.3794; 0.9675] 9.6
## age:intervention
## S1 adolescent:Gender-stereotype color, ranking, badges, and avatar
## S2 adolescent:Gender-stereotype color, ranking, badges, and avatar
## S3 adolescent:Gender-stereotype color, ranking, badges, and avatar
## S4 adult:Gender-stereotype color, ranking, badges, and avatar
## S5 adult:Gender-stereotype color, ranking, badges, and avatar
## S6 adult:Gender-stereotype color, ranking, badges, and avatar
## S7 adult:Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU no-restriction:Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017) no-restriction:Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs adolescence:Gender-stereotyped motivational message prompts
##
## Number of studies combined: k = 10
## Number of observations: o = 360
##
## SMD 95%-CI t p-value
## Random effects model 0.0694 [-0.1595; 0.2982] 0.69 0.5101
##
## Quantifying heterogeneity:
## tau^2 = 0 [0.0000; 0.2388]; tau = 0 [0.0000; 0.4887]
## I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
##
## Test of heterogeneity:
## Q d.f. p-value
## 8.11 9 0.5232
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q I^2
## age:intervention = adolescent:Gender-stereotype co ... 3 -0.2102 [-0.6073; 0.1869] 0 0 0.52 0.0%
## age:intervention = adult:Gender-stereotype color, ... 4 0.3633 [ 0.1338; 0.5928] 0 0 0.52 0.0%
## age:intervention = no-restriction:Gender-stereotyp ... 2 -0.1261 [-2.9511; 2.6989] 0 0 0.78 0.0%
## age:intervention = adolescence:Gender-stereotyped ... 1 0.2941 [-0.3794; 0.9675] -- -- 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 25.61 3 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.sg4sub <- update.meta(m.cont, subgroup = `ed.level:intervention`, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance for women in stMale
##
## SMD 95%-CI %W(random)
## S1 -0.3420 [-0.9865; 0.3026] 10.5
## S2 -0.0614 [-0.6009; 0.4780] 15.0
## S3 -0.3018 [-0.9889; 0.3852] 9.2
## S4 0.4275 [-0.2948; 1.1499] 8.4
## S5 0.1428 [-0.5485; 0.8341] 9.1
## S6 0.4443 [-0.2942; 1.1829] 8.0
## S7 0.4315 [-0.1676; 1.0305] 12.2
## S8: Conducted by BNU 0.0743 [-0.5888; 0.7374] 9.9
## S9: Albuquerque, et al. (2017) -0.3727 [-1.1083; 0.3628] 8.1
## S10: Only use prompt msgs 0.2941 [-0.3794; 0.9675] 9.6
## ed.level:intervention
## S1 upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S2 upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S3 upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S4 higher-education:Gender-stereotype color, ranking, badges, and avatar
## S5 higher-education:Gender-stereotype color, ranking, badges, and avatar
## S6 higher-education:Gender-stereotype color, ranking, badges, and avatar
## S7 unknown:Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU unknown:Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017) unknown:Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs upper-secundary:Gender-stereotyped motivational message prompts
##
## Number of studies combined: k = 10
## Number of observations: o = 360
##
## SMD 95%-CI t p-value
## Random effects model 0.0694 [-0.1595; 0.2982] 0.69 0.5101
##
## Quantifying heterogeneity:
## tau^2 = 0 [0.0000; 0.2388]; tau = 0 [0.0000; 0.4887]
## I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
##
## Test of heterogeneity:
## Q d.f. p-value
## 8.11 9 0.5232
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau
## ed.level:intervention = upper-secundary:Gender-stereoty ... 3 -0.2102 [-0.6073; 0.1869] 0 0
## ed.level:intervention = higher-education:Gender-stereot ... 3 0.3308 [-0.0970; 0.7586] 0 0
## ed.level:intervention = unknown:Gender-stereotype color ... 3 0.0840 [-0.9049; 1.0730] 0.0431 0.2077
## ed.level:intervention = upper-secundary:Gender-stereoty ... 1 0.2941 [-0.3794; 0.9675] -- --
## Q I^2
## ed.level:intervention = upper-secundary:Gender-stereoty ... 0.52 0.0%
## ed.level:intervention = higher-education:Gender-stereot ... 0.44 0.0%
## ed.level:intervention = unknown:Gender-stereotype color ... 2.77 27.8%
## ed.level:intervention = upper-secundary:Gender-stereoty ... 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 16.45 3 0.0009
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.sg4sub <- update.meta(m.cont, subgroup = `country:age:intervention`, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance for women in stMale
##
## SMD 95%-CI %W(random)
## S1 -0.3420 [-0.9865; 0.3026] 10.5
## S2 -0.0614 [-0.6009; 0.4780] 15.0
## S3 -0.3018 [-0.9889; 0.3852] 9.2
## S4 0.4275 [-0.2948; 1.1499] 8.4
## S5 0.1428 [-0.5485; 0.8341] 9.1
## S6 0.4443 [-0.2942; 1.1829] 8.0
## S7 0.4315 [-0.1676; 1.0305] 12.2
## S8: Conducted by BNU 0.0743 [-0.5888; 0.7374] 9.9
## S9: Albuquerque, et al. (2017) -0.3727 [-1.1083; 0.3628] 8.1
## S10: Only use prompt msgs 0.2941 [-0.3794; 0.9675] 9.6
## country:age:intervention
## S1 Brazil:adolescent:Gender-stereotype color, ranking, badges, and avatar
## S2 Brazil:adolescent:Gender-stereotype color, ranking, badges, and avatar
## S3 Brazil:adolescent:Gender-stereotype color, ranking, badges, and avatar
## S4 Brazil:adult:Gender-stereotype color, ranking, badges, and avatar
## S5 Brazil:adult:Gender-stereotype color, ranking, badges, and avatar
## S6 Brazil:adult:Gender-stereotype color, ranking, badges, and avatar
## S7 Brazil:adult:Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU China:no-restriction:Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017) Brazil:no-restriction:Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs Brazil:adolescence:Gender-stereotyped motivational message prompts
##
## Number of studies combined: k = 10
## Number of observations: o = 360
##
## SMD 95%-CI t p-value
## Random effects model 0.0694 [-0.1595; 0.2982] 0.69 0.5101
##
## Quantifying heterogeneity:
## tau^2 = 0 [0.0000; 0.2388]; tau = 0 [0.0000; 0.4887]
## I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
##
## Test of heterogeneity:
## Q d.f. p-value
## 8.11 9 0.5232
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q
## country:age:intervention = Brazil:adolescent:Gender-stereo ... 3 -0.2102 [-0.6073; 0.1869] 0 0 0.52
## country:age:intervention = Brazil:adult:Gender-stereotype ... 4 0.3633 [ 0.1338; 0.5928] 0 0 0.52
## country:age:intervention = China:no-restriction:Gender-ste ... 1 0.0743 [-0.5888; 0.7374] -- -- 0.00
## country:age:intervention = Brazil:no-restriction:Gender-st ... 1 -0.3727 [-1.1083; 0.3628] -- -- 0.00
## country:age:intervention = Brazil:adolescence:Gender-stere ... 1 0.2941 [-0.3794; 0.9675] -- -- 0.00
## I^2
## country:age:intervention = Brazil:adolescent:Gender-stereo ... 0.0%
## country:age:intervention = Brazil:adult:Gender-stereotype ... 0.0%
## country:age:intervention = China:no-restriction:Gender-ste ... --
## country:age:intervention = Brazil:no-restriction:Gender-st ... --
## country:age:intervention = Brazil:adolescence:Gender-stere ... --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 26.09 4 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.sg4sub <- update.meta(m.cont, subgroup = `country:ed.level:intervention`, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance for women in stMale
##
## SMD 95%-CI %W(random)
## S1 -0.3420 [-0.9865; 0.3026] 10.5
## S2 -0.0614 [-0.6009; 0.4780] 15.0
## S3 -0.3018 [-0.9889; 0.3852] 9.2
## S4 0.4275 [-0.2948; 1.1499] 8.4
## S5 0.1428 [-0.5485; 0.8341] 9.1
## S6 0.4443 [-0.2942; 1.1829] 8.0
## S7 0.4315 [-0.1676; 1.0305] 12.2
## S8: Conducted by BNU 0.0743 [-0.5888; 0.7374] 9.9
## S9: Albuquerque, et al. (2017) -0.3727 [-1.1083; 0.3628] 8.1
## S10: Only use prompt msgs 0.2941 [-0.3794; 0.9675] 9.6
## country:ed.level:intervention
## S1 Brazil:upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S2 Brazil:upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S3 Brazil:upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S4 Brazil:higher-education:Gender-stereotype color, ranking, badges, and avatar
## S5 Brazil:higher-education:Gender-stereotype color, ranking, badges, and avatar
## S6 Brazil:higher-education:Gender-stereotype color, ranking, badges, and avatar
## S7 Brazil:unknown:Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU China:unknown:Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017) Brazil:unknown:Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs Brazil:upper-secundary:Gender-stereotyped motivational message prompts
##
## Number of studies combined: k = 10
## Number of observations: o = 360
##
## SMD 95%-CI t p-value
## Random effects model 0.0694 [-0.1595; 0.2982] 0.69 0.5101
##
## Quantifying heterogeneity:
## tau^2 = 0 [0.0000; 0.2388]; tau = 0 [0.0000; 0.4887]
## I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]
##
## Test of heterogeneity:
## Q d.f. p-value
## 8.11 9 0.5232
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2
## country:ed.level:intervention = Brazil:upper-secundary:Gender-s ... 3 -0.2102 [-0.6073; 0.1869] 0
## country:ed.level:intervention = Brazil:higher-education:Gender- ... 3 0.3308 [-0.0970; 0.7586] 0
## country:ed.level:intervention = Brazil:unknown:Gender-stereotyp ... 2 0.0589 [-5.0366; 5.1543] 0.2062
## country:ed.level:intervention = China:unknown:Gender-stereotype ... 1 0.0743 [-0.5888; 0.7374] --
## country:ed.level:intervention = Brazil:upper-secundary:Gender-s ... 1 0.2941 [-0.3794; 0.9675] --
## tau Q I^2
## country:ed.level:intervention = Brazil:upper-secundary:Gender-s ... 0 0.52 0.0%
## country:ed.level:intervention = Brazil:higher-education:Gender- ... 0 0.44 0.0%
## country:ed.level:intervention = Brazil:unknown:Gender-stereotyp ... 0.4541 2.76 63.8%
## country:ed.level:intervention = China:unknown:Gender-stereotype ... -- 0.00 --
## country:ed.level:intervention = Brazil:upper-secundary:Gender-s ... -- 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 16.44 4 0.0025
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = paste0(gender,':',env))
m.cont <- update.meta(m.cont, studlab = data$study)
summary(eggers.test(x = m.cont))## Eggers' test of the intercept
## =============================
##
## intercept 95% CI t p
## 0.692 -5.72 - 7.11 0.212 0.84
##
## Eggers' test does not indicate the presence of funnel plot asymmetry.funnel(m.cont, xlab = "Hedges' g", studlab = T, legend=T, addtau2 = T)